Predicting gastrointestinal bleeding and audio biomarkers based on machine learning analysis of bowel sounds

Table 1 Baseline characteristics of the upper gastrointestinal bleeding group and the healthy control group

	Age			Sex
	Median	Lower quartile	Upper quartile	Male	Female
UGIB patients	59.00	45.50	67.75	22	18
Healthy controls	58.50	46.75	66.75	22	18
P value	> 0.05			> 0.05

UGIB: Upper gastrointestinal bleeding.

Table 2 Biochemical indicators in patients with upper gastrointestinal bleeding

	Median	Lower quartile	Upper quartile	Medical reference range
Red blood cell count, 1012/L	3.05	2.77	3.51	4.3-5.8
Hemoglobin, g/L	90.5	83	105	130-175
Hematocrit, %	29.6	25.7	32.4	40-50
CRP, mg/L	6.8	6	24.65	0-10
hs-CRP, mg/L	6	2.84	6	0.3

CRP: C-reactive protein; hs-CRP: High-sensitivity C-reactive protein.

Table 3 Reference for the characteristics of bowel sounds

Feature type	Feature	Physical meaning
Time domain	Max, Min, Mean, Peak_to_Peak	Amplitude peak, minimum, arithmetic mean value, difference between maximum and minimum
	Abs_Mean, Variance, Std_Dev, RMS	Amplitude absolute value average, variance, standard, root mean square
	Waveform_Length, Skewness	Cumulative change magnitude, signal distribution asymmetry
	Kurtosis, Crest_Factor	Signal peakiness, normalized fourth moment
	Zero_Crossing_Rate_Mean, Varian, Skewne	Signal oscillation frequency, oscillation degree, oscillation skewness characteristic
	Q1, Q3, interquartile range	Amplitude 25th, 75th percentile value, range between 25th and 75th
	Shape_Factor, Impluse_Factor	Regularity, peak component intensity of signals
	Margin_Factor, Hjorth_Activity, Hjorth Mobility	Signal large amplitude change index, the total power of the signal, contraction frequency fluctuation
	Autocoor_Peak, Fractal_Dimension, Energy, Avg_energy	Maximum signal self-similarity, signal complexity/irregularity, total squared amplitude, energy per sample
Frequency domain	Spectral_Centroid, Slope, Bandwidth, Flatness	Signal frequency, variance around of centroid, tonal vs noise-like character
	Spectral_Rolloff85, Rolloff95, Steepness	Frequency point with 85%, 95% total energy, the average energy ratio of high frequency to low frequency
	Band_Energy_[1-5]	50-100 Hz, 100-500 Hz, 500-1000 Hz, 1000-2000 Hz, 2000-4000 Hz
	MFCC_Mean_[1-13]	Spectral envelope characteristics
	MFCC_Var_[1-13]	Spread of spectral envelope
	Spectral Steepness, PSD_Peak, Harmonic_Ratio	Average spectral slope, maximum power spectral density, harmonic content proportion
Time-frequency domain	Wavelet_Energy_[0-2]	Wavelet coefficient energy
	Wavelet_Kurtosis_[0-2]	Peakiness in wavelet coefficients
	STFT_Energy_Kurtosis	Peak concentration of short-time Fourier transform energy
Nonlinear characteristics	Hjorth_Complexity	The complexity of signal changes
	Wavelet_Entropy_[0-2], STFT_Energy_Entropy	Signal complexity in wavelet domain, uncertainty in time-frequency energy distribution

Table 4 Results of full feature machine learning model

Algorithms	Accuracy	F1 score	Sensitivity	Specificity
SVM	0.81	0.793	0.742	0.875
RF	0.714	0.719	0.742	0.688
KNN	0.698	0.678	0.645	0.75
LR	0.789	0.783	0.766	0.813

SVM: Support vector machine; RF: Random forest; KNN: K-nearest neighbors; LR: Logistic regression.

Table 5 Specific sensitivity results of feature subset machine learning

	Sensitivity					Specificity					Accuracy					F1 score
Model	40	35	30	25	20	40	35	30	25	20	40	35	30	25	20	40	35	30	25	20
SVM	0.581	0.774	0.71	0.71	0.71	0.813	0.875	0.875	0.906	0.844	0.698	0.826	0.794	0.81	0.778	0.655	0.814	0.772	0.786	0.759
RF	0.677	0.742	0.742	0.677	0.71	0.688	0.719	0.719	0.719	0.813	0.683	0.73	0.73	0.698	0.762	0.677	0.73	0.73	0.689	0.746
KNN	0.58	0.713	0.516	0.613	0.581	0.75	0.813	0.719	0.719	0.75	0.667	0.714	0.619	0.667	0.667	0.632	0.679	0.571	0.644	0.632
LR	0.702	0.713	0.723	0.713	0.744	0.854	0.854	0.875	0.865	0.875	0.789	0.779	0.8	0.789	0.811	0.773	0.759	0.782	0.767	0.795

SVM: Support vector machine; RF: Random forest; KNN: K-nearest neighbors; LR: Logistic regression.